Combining spectral and fractal features for emotion recognition on Electroencephalographic signals

Recent studies have attempted to recognize emotions by extracting spectral and fractal features from electroencephalographic signals; however, up to now none of them have combined these two features to recognize emotions. This paper aims at providing a comparison between an accuracy rate of an approach that recognizes emotions by extracting both spectral and fractal features with that of those that extract only one of these features. To this end, we designed and implemented a procedure that recognizes positive and negative emotions by extracting spectral, fractal, or both features. Next, using this procedure, we built three different approaches to recognize positive and negative emotions; the first one extracted both spectral and fractal features, whereas the other two extracted each type of feature separately.

• Autores: Ulloa Villegas, Gonzalo Vicente; Valderrama, Camilo E.
• Tipo de recurso: Article of investigation
• Fecha de publicación: 2014
• Institución: Universidad ICESI
• Repositorio: Repositorio ICESI
• Idioma: eng
• OAI Identifier: oai:repository.icesi.edu.co:10906/82313
• Acceso en línea: https://nebulosa.icesi.edu.co:2180/record/display.uri?eid=2-s2.0-84905403981&origin=resultslist&sort=plf-f&src=s&st1=Combining+spectral+and+fractal+features+for+emotion+recognition+on+Electroencephalographic+signals&st2=&sid=3202df997427afbc60b94886b40ced79&sot=b&sdt=b&sl=113&s=TITLE-ABS-KEY%28Combining+spectral+and+fractal+features+for+emotion+recognition+on+Electroencephalographic+signals%29&relpos=0&citeCnt=0&searchTerm=; https://www.semanticscholar.org/paper/Combining-spectral-and-fractal-features-for-emotio-Valderrama-Ulloa/b058db4685e71c91245a609c54d7bc71f35e7b43; http://hdl.handle.net/10906/82313
• Palabra clave: Computación; Ingeniería de sistemas y comunicaciones; Systems engineering; Procedimiento
• Rights: openAccess
• License: https://creativecommons.org/licenses/by-nc-nd/4.0/